Research on UAV network slicing resource management for low-altitude intelligent network

In low-altitude intelligent networks, unmanned aerial vehicles (UAV) play a crucial role as aerial communication base stations, data relay nodes, and mobile network terminals. Leveraging their exceptional mobility and adaptability, UAV can extend network coverage and support a wide range of service applications. However, the challenges of dynamic network topology, constrained airspace resources, and diverse service demand pose significant difficulties in achieving efficient resource orchestration and management. To address these challenges, an end-to-end slicing approach for UAV network was introduced, enabling the construction of logical network architectures tailored to specific requirements. A cluster trajectory prediction model was developed to identify the positions of clustered access nodes, providing essential support for resource reservation and optimization in network slicing. Building on this, a dual time-scale resource management framework was proposed. At a larger time scale, the slice reconfiguration problem was transformed into a constrained optimization task by a nonlinear programming approach, maximizing overall slice efficiency and ensuring rational resource reservation. At a finer time scale, intra-slice resource scheduling strategies were implemented to meet the QoS requirements of specific services. Simulation results demonstrate that the proposed method significantly improves the communication performance of low-altitude dynamic intelligent networks. It enhances the adaptability and service quality of UAV network slicing in dynamic environments, offering effective support for resource management and service assurance in complex scenarios.